Regret bounds for online portfolio selection with a cardinality constraint

Shinji Ito, Daisuke Hatano, Hanna Sumita, Akihiro Yabe, Takuro Fukunaga, Naonori Kakimura, Ken Ichi Kawarabayashi

研究成果: Conference article査読

3 被引用数 (Scopus)

抄録

Online portfolio selection is a sequential decision-making problem in which a learner repetitively selects a portfolio over a set of assets, aiming to maximize long-term return. In this paper, we study the problem with the cardinality constraint that the number of assets in a portfolio is restricted to be at most k, and consider two scenarios: (i) in the full-feedback setting, the learner can observe price relatives (rates of return to cost) for all assets, and (ii) in the bandit-feedback setting, the learner can observe price relatives only for invested assets. We propose efficient algorithms for these scenarios, which achieve sublinear regrets. We also provide regret (statistical) lower bounds for both scenarios which nearly match the upper bounds when k is a constant. In addition, we give a computational lower bound, which implies that no algorithm maintains both computational efficiency, as well as a small regret upper bound.

本文言語English
ページ(範囲)10588-10597
ページ数10
ジャーナルAdvances in Neural Information Processing Systems
2018-December
出版ステータスPublished - 2018
イベント32nd Conference on Neural Information Processing Systems, NeurIPS 2018 - Montreal, Canada
継続期間: 2018 12 22018 12 8

ASJC Scopus subject areas

  • コンピュータ ネットワークおよび通信
  • 情報システム
  • 信号処理

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